A Code Recommendation Method Using RNN Based on Interaction History


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 12, pp. 461-468, Dec. 2018
https://doi.org/10.3745/KTSDE.2018.7.12.461,   PDF Download:
Keywords: software engineering, Deep Learning, Interaction History
Abstract

Developers spend a significant amount of time exploring and trying to understand source code to find a source location to modify. To reduce such time, existing studies have recommended the source location using statistical language model techniques. However, in these techniques, the recommendation does not occur if input data does not exactly match with learned data. In this paper, we propose a code location recommendation method using Recurrent Neural Networks and interaction histories, which does not have the above problem of the existing techniques. Our method achieved an average precision of 91% and an average recall of 71%, thereby reducing time for searching and exploring code more than the existing recommendation techniques.


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Cite this article
[IEEE Style]
H. Cho, S. Lee, S. Kang, "A Code Recommendation Method Using RNN Based on Interaction History," KIPS Transactions on Software and Data Engineering, vol. 7, no. 12, pp. 461-468, 2018. DOI: https://doi.org/10.3745/KTSDE.2018.7.12.461.

[ACM Style]
Heetae Cho, Seonah Lee, and Sungwon Kang. 2018. A Code Recommendation Method Using RNN Based on Interaction History. KIPS Transactions on Software and Data Engineering, 7, 12, (2018), 461-468. DOI: https://doi.org/10.3745/KTSDE.2018.7.12.461.